OpenRaiser/Intern-Atlas huggingface dataset:
(Meta) Competences for Digital Practice: Educational Scenarios for the Workplace of the Future Exemplified by Building Information Modeling Work Processes
Venue: arXiv cs.CY 2022
Year: 2022

Workplaces of the future require advanced competence profiles from employees, not least due to new options for teleworking and new complex digital tools. The acquisition of advanced competence profiles is to be addressed by formal education. For example, the method of Building Information Modeling (BIM) aims at digitizing the design, construction, and operation of structures and as such requires advanced competence profiles. In this study, two educational scenarios based on teleworking and complex digital tools are compared, each with one cohort and consisting of two learning activities. The first cohort initially completes as first learning activity a semester-long course that aims at BIM domain competences. The semester-long course of the second cohort fosters meta competences, such as communication, collaboration, and digital literacy. At the end of the semester, both cohorts solve in a second learning activity a BIM practice task. Research questions are: (1) Do the two educational scenarios promote the competences to be addressed? And related: (2) What is the impact of the initial course that fosters domain competences or meta competences? Methodologically, the learning outcomes are assessed by measuring the domain competences three times during the educational scenario using online tests in the two cohorts (N=11). Further, students' perceptions are surveyed in parallel using online questionnaires. In addition, semi-structured interviews are conducted at the end of the educational scenarios. The quantitative and qualitative results of the study designating the training of meta competencies partly as a substitute for imparting domain competences are presented. Further, the influence of both educational scenarios on competence development for the workplace of the future characterized by telework and digital tools is discussed.
Proteus: A Self-Designing Range Filter
Venue: arXiv cs.DB 2022
Year: 2022

We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given space requirement. Proteus unifies the probabilistic and deterministic design spaces of state-of-the-art range filters to achieve robust performance across a larger variety of use cases. At the core of Proteus lies our Contextual Prefix FPR (CPFPR) model - a formal framework for the FPR of prefix-based filters across their design spaces. We empirically demonstrate the accuracy of our model and Proteus' ability to optimize over both synthetic workloads and real-world datasets. We further evaluate Proteus in RocksDB and show that it is able to improve end-to-end performance by as much as 5.3x over more brittle state-of-the-art methods such as SuRF and Rosetta. Our experiments also indicate that the cost of modeling is not significant compared to the end-to-end performance gains and that Proteus is robust to workload shifts.
Mitigating Sovereign Data Exchange Challenges: A Mapping to Apply Privacy- and Authenticity-Enhancing Technologies
Venue: arXiv cs.CY 2022
Year: 2022

Harmful repercussions from sharing sensitive or personal data can hamper institutions' willingness to engage in data exchange. Thus, institutions consider Authenticity Enhancing Technologies (AETs) and Privacy-Enhancing Technologies (PETs) to engage in Sovereign Data Exchange (SDE), i.e., sharing data with third parties without compromising their own or their users' data sovereignty. However, these technologies are often technically complex, which impedes their adoption. To support practitioners select PETs and AETs for SDE use cases and highlight SDE challenges researchers and practitioners should address, this study empirically constructs a challenge-oriented technology mapping. First, we compile challenges of SDE by conducting a systematic literature review and expert interviews. Second, we map PETs and AETs to the SDE challenges and identify which technologies can mitigate which challenges. We validate the mapping through investigator triangulation. Although the most critical challenge concerns data usage and access control, we find that the majority of PETs and AETs focus on data processing issues.
On Finding Pure Nash Equilibria of Discrete Preference Games and Network Coordination Games
Venue: arXiv cs.GT 2022
Year: 2022

This paper deals with the complexity of the problem of computing a pure Nash equilibrium for discrete preference games and network coordination games beyond $O(\log n)$-treewidth and tree metric spaces. First, we estimate the number of iterations of the best response dynamics for a discrete preference game on a discrete metric space with at least three strategies. Second, we present a sufficient condition that we have a polynomial-time algorithm to find a pure Nash equilibrium for a discrete preference game on a grid graph. Finally, we discuss the complexity of finding a pure Nash equilibrium for a two-strategic network coordination game whose cost functions satisfy submodularity. In this case, if every cost function is symmetric, the games are polynomial-time reducible to a discrete preference game on a path metric space.
Wild Networks: Exposure of 5G Network Infrastructures to Adversarial Examples
Venue: arXiv cs.CR 2022
Year: 2022

Fifth Generation (5G) networks must support billions of heterogeneous devices while guaranteeing optimal Quality of Service (QoS). Such requirements are impossible to meet with human effort alone, and Machine Learning (ML) represents a core asset in 5G. ML, however, is known to be vulnerable to adversarial examples; moreover, as our paper will show, the 5G context is exposed to a yet another type of adversarial ML attacks that cannot be formalized with existing threat models. Proactive assessment of such risks is also challenging due to the lack of ML-powered 5G equipment available for adversarial ML research. To tackle these problems, we propose a novel adversarial ML threat model that is particularly suited to 5G scenarios, and is agnostic to the precise function solved by ML. In contrast to existing ML threat models, our attacks do not require any compromise of the target 5G system while still being viable due to the QoS guarantees and the open nature of 5G networks. Furthermore, we propose an original framework for realistic ML security assessments based on public data. We proactively evaluate our threat model on 6 applications of ML envisioned in 5G. Our attacks affect both the training and the inference stages, can degrade the performance of state-of-the-art ML systems, and have a lower entry barrier than previous attacks.
General Policy Evaluation and Improvement by Learning to Identify Few But Crucial States
Venue: arXiv cs.LG 2022
Year: 2022

Learning to evaluate and improve policies is a core problem of Reinforcement Learning (RL). Traditional RL algorithms learn a value function defined for a single policy. A recently explored competitive alternative is to learn a single value function for many policies. Here we combine the actor-critic architecture of Parameter-Based Value Functions and the policy embedding of Policy Evaluation Networks to learn a single value function for evaluating (and thus helping to improve) any policy represented by a deep neural network (NN). The method yields competitive experimental results. In continuous control problems with infinitely many states, our value function minimizes its prediction error by simultaneously learning a small set of `probing states' and a mapping from actions produced in probing states to the policy's return. The method extracts crucial abstract knowledge about the environment in form of very few states sufficient to fully specify the behavior of many policies. A policy improves solely by changing actions in probing states, following the gradient of the value function's predictions. Surprisingly, it is possible to clone the behavior of a near-optimal policy in Swimmer-v3 and Hopper-v3 environments only by knowing how to act in 3 and 5 such learned states, respectively. Remarkably, our value function trained to evaluate NN policies is also invariant to changes of the policy architecture: we show that it allows for zero-shot learning of linear policies competitive with the best policy seen during training. Our code is public.
Recommendation Systems with Distribution-Free Reliability Guarantees
Venue: arXiv cs.IR 2022
Year: 2022

When building recommendation systems, we seek to output a helpful set of items to the user. Under the hood, a ranking model predicts which of two candidate items is better, and we must distill these pairwise comparisons into the user-facing output. However, a learned ranking model is never perfect, so taking its predictions at face value gives no guarantee that the user-facing output is reliable. Building from a pre-trained ranking model, we show how to return a set of items that is rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate (FDR), regardless of the (unknown) data distribution. Moreover, our calibration algorithm enables the easy and principled integration of multiple objectives in recommender systems. As an example, we show how to optimize for recommendation diversity subject to a user-specified level of FDR control, circumventing the need to specify ad hoc weights of a diversity loss against an accuracy loss. Throughout, we focus on the problem of learning to rank a set of possible recommendations, evaluating our methods on the Yahoo! Learning to Rank and MSMarco datasets.
A Framework for Auditing Multilevel Models using Explainability Methods
Venue: arXiv cs.CY 2022
Year: 2022

Applications of multilevel models usually result in binary classification within groups or hierarchies based on a set of input features. For transparent and ethical applications of such models, sound audit frameworks need to be developed. In this paper, an audit framework for technical assessment of regression MLMs is proposed. The focus is on three aspects, model, discrimination, and transparency and explainability. These aspects are subsequently divided into sub aspects. Contributors, such as inter MLM group fairness, feature contribution order, and aggregated feature contribution, are identified for each of these sub aspects. To measure the performance of the contributors, the framework proposes a shortlist of KPIs. A traffic light risk assessment method is furthermore coupled to these KPIs. For assessing transparency and explainability, different explainability methods (SHAP and LIME) are used, which are compared with a model intrinsic method using quantitative methods and machine learning modelling. Using an open source dataset, a model is trained and tested and the KPIs are computed. It is demonstrated that popular explainability methods, such as SHAP and LIME, underperform in accuracy when interpreting these models. They fail to predict the order of feature importance, the magnitudes, and occasionally even the nature of the feature contribution. For other contributors, such as group fairness and their associated KPIs, similar analysis and calculations have been performed with the aim of adding profundity to the proposed audit framework. The framework is expected to assist regulatory bodies in performing conformity assessments of AI systems using multilevel binomial classification models at businesses. It will also benefit businesses deploying MLMs to be future proof and aligned with the European Commission proposed Regulation on Artificial Intelligence.
Service Modeling and Delay Analysis of Packet Delivery over a Wireless Link
Venue: arXiv cs.PF 2022
Year: 2022

For delay analysis of packet delivery over a wireless link, several novel ideas are introduced. One is to construct an equivalent $G/G/1$ non-lossy queueing model to ease the analysis, enabled by exploiting empirical models of packet error rate, packet service time and packet loss rate obtained from measurement. The second is to exploit a classical queueing theory result to approximate the mean delay. For estimating the delay distribution, the newly developed stochastic network calculus (SNC) theory is made use of, forming the third idea. To enable this SNC based analysis, a stochastic service curve characterization of the link is introduced, relying on a packet service time model obtained from the empirical models. The focused link is a 802.15.4 wireless link. Extensive experimental investigation under a wide range of settings was conducted. The proposed ideas are validated with the experiment results. The validation confirms that the proposed approaches, integrating both empirical and analytical modes, are effective for service modeling and delay analysis. This suggests an integrated approach, now found previously, for quantitative understanding of the delay performance of packet delivery over a wireless link.
Sea: A lightweight data-placement library for Big Data scientific computing
Venue: arXiv cs.DC 2022
Year: 2022

The recent influx of open scientific data has contributed to the transitioning of scientific computing from compute intensive to data intensive. Whereas many Big Data frameworks exist that minimize the cost of data transfers, few scientific applications integrate these frameworks or adopt data-placement strategies to mitigate the costs. Scientific applications commonly rely on well-established command-line tools that would require complete reinstrumentation in order to incorporate existing frameworks. We developed Sea as a means to enable data-placement strategies for scientific applications executing on HPC clusters without the need to reinstrument workflows. Sea leverages GNU C library interception to intercept POSIX-compliant file system calls made by the applications. We designed a performance model and evaluated the performance of Sea on a synthetic data-intensive application processing a representative neuroimaging dataset (the Big Brain). Our results demonstrate that Sea significantly improves performance, up to a factor of 3$\times$.
Topic Community Based Temporal Expertise for Question Routing
Venue: arXiv cs.SI 2022
Year: 2022

Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: 1) sparsity of users' past records results in lack of personalized recommendation that at times does not match users' interest or domain expertise, 2) modeling based on all questions and answers content makes periodic updates computationally expensive, and 3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users' activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution
Venue: arXiv cs.CV 2022
Year: 2022

Despite the quality improvement brought by the recent methods, video super-resolution (SR) is still very challenging, especially for videos that are low-light and noisy. The current best solution is to subsequently employ best models of video SR, denoising, and illumination enhancement, but doing so often lowers the image quality, due to the inconsistency between the models. This paper presents a new parametric representation called the Deep Parametric 3D Filters (DP3DF), which incorporates local spatiotemporal information to enable simultaneous denoising, illumination enhancement, and SR efficiently in a single encoder-and-decoder network. Also, a dynamic residual frame is jointly learned with the DP3DF via a shared backbone to further boost the SR quality. We performed extensive experiments, including a large-scale user study, to show our method's effectiveness. Our method consistently surpasses the best state-of-the-art methods on all the challenging real datasets with top PSNR and user ratings, yet having a very fast run time.
Challenges in Adapting ECH in TLS for Privacy Enhancement over the Internet
Venue: arXiv cs.CR 2022
Year: 2022

Security and Privacy are crucial in modern Internet services. Transport Layer Security (TLS) has largely addressed the issue of security. However, information about the type of service being accessed goes in plain-text in the initial handshakes of vanilla TLS, thus potentially revealing the activity of users and compromising privacy. The ``Encrypted ClientHello'' or ECH overcomes this issue by extending TLS 1.3 where all of the information that can potentially reveal the service type is masked, thus addressing the privacy issues in TLS 1.3. However, we notice that Internet services tend to use different versions of TLS for application data (primary connection/channel) and supporting data (side channels) such as scheduling information \textit{etc.}. %, during the active session. Although many internet services have migrated to TLS 1.3, we notice that it is only true for the primary connections which do benefit from TLS 1.3, while the side-channels continue to use lower version of TLS (e.g., 1.2) %which do not support ECH and continue to leak type of service accessed. We demonstrate that privacy information leaked from the side-channels can be used to affect the performance on the primary channels, like blocking or throttling specific service on the internet. Our work demonstrates that adapting ECH on primary channels alone is not sufficient to prevent the privacy leaks and attacks on primary channels. Further, we demonstrate that it is necessary for all of the associated side-channels also to migrate to TLS 1.3 and adapt ECH extension in order to offer complete privacy preservatio
ORF-Net: Deep Omni-supervised Rib Fracture Detection from Chest CT Scans
Venue: arXiv cs.CV 2022
Year: 2022

Most of the existing object detection works are based on the bounding box annotation: each object has a precise annotated box. However, for rib fractures, the bounding box annotation is very labor-intensive and time-consuming because radiologists need to investigate and annotate the rib fractures on a slice-by-slice basis. Although a few studies have proposed weakly-supervised methods or semi-supervised methods, they could not handle different forms of supervision simultaneously. In this paper, we proposed a novel omni-supervised object detection network, which can exploit multiple different forms of annotated data to further improve the detection performance. Specifically, the proposed network contains an omni-supervised detection head, in which each form of annotation data corresponds to a unique classification branch. Furthermore, we proposed a dynamic label assignment strategy for different annotated forms of data to facilitate better learning for each branch. Moreover, we also design a confidence-aware classification loss to emphasize the samples with high confidence and further improve the model's performance. Extensive experiments conducted on the testing dataset show our proposed method outperforms other state-of-the-art approaches consistently, demonstrating the efficacy of deep omni-supervised learning on improving rib fracture detection performance.
Betti numbers of attention graphs is all you really need
Venue: arXiv cs.CL 2022
Year: 2022

We apply methods of topological analysis to the attention graphs, calculated on the attention heads of the BERT model ( arXiv:1810.04805v2 ). Our research shows that the classifier built upon basic persistent topological features (namely, Betti numbers) of the trained neural network can achieve classification results on par with the conventional classification method. We show the relevance of such topological text representation on three text classification benchmarks. For the best of our knowledge, it is the first attempt to analyze the topology of an attention-based neural network, widely used for Natural Language Processing.
GLANCE: Global to Local Architecture-Neutral Concept-based Explanations
Venue: arXiv cs.CV 2022
Year: 2022

Most of the current explainability techniques focus on capturing the importance of features in input space. However, given the complexity of models and data-generating processes, the resulting explanations are far from being `complete', in that they lack an indication of feature interactions and visualization of their `effect'. In this work, we propose a novel twin-surrogate explainability framework to explain the decisions made by any CNN-based image classifier (irrespective of the architecture). For this, we first disentangle latent features from the classifier, followed by aligning these features to observed/human-defined `context' features. These aligned features form semantically meaningful concepts that are used for extracting a causal graph depicting the `perceived' data-generating process, describing the inter- and intra-feature interactions between unobserved latent features and observed `context' features. This causal graph serves as a global model from which local explanations of different forms can be extracted. Specifically, we provide a generator to visualize the `effect' of interactions among features in latent space and draw feature importance therefrom as local explanations. Our framework utilizes adversarial knowledge distillation to faithfully learn a representation from the classifiers' latent space and use it for extracting visual explanations. We use the styleGAN-v2 architecture with an additional regularization term to enforce disentanglement and alignment. We demonstrate and evaluate explanations obtained with our framework on Morpho-MNIST and on the FFHQ human faces dataset. Our framework is available at \url{https://github.com/koriavinash1/GLANCE-Explanations}.
MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering
Venue: arXiv cs.CL 2022
Year: 2022

We describe our two-stage system for the Multilingual Information Access (MIA) 2022 Shared Task on Cross-Lingual Open-Retrieval Question Answering. The first stage consists of multilingual passage retrieval with a hybrid dense and sparse retrieval strategy. The second stage consists of a reader which outputs the answer from the top passages returned by the first stage. We show the efficacy of using a multilingual language model with entity representations in pretraining, sparse retrieval signals to help dense retrieval, and Fusion-in-Decoder. On the development set, we obtain 43.46 F1 on XOR-TyDi QA and 21.99 F1 on MKQA, for an average F1 score of 32.73. On the test set, we obtain 40.93 F1 on XOR-TyDi QA and 22.29 F1 on MKQA, for an average F1 score of 31.61. We improve over the official baseline by over 4 F1 points on both the development and test sets.
Static Deadlock Detection in Low-Level C Code
Venue: arXiv cs.SE 2022
Year: 2022

We present a novel scalable deadlock analyser L2D2 capable of handling C code with low-level unstructured lock manipulation. L2D2 runs along the call tree of a program, starting from its leaves, and analyses each function just once, without any knowledge of the call context. L2D2 builds function summaries recording information about locks that are assumed or known to be locked or unlocked at the entry, inside, and at the exit of functions, together with lock dependencies, and reports warnings about possible deadlocks when cycles in the lock dependencies are detected. We implemented L2D2 as a plugin of the Facebook/Meta Infer framework and report results of experiments on a large body of C as well as C++ code illustrating the effectiveness and efficiency of L2D2.